Location determination using RF fingerprinting

ABSTRACT

A method for determining the location of a mobile unit (mobile unit) in a wireless communication system is disclosed. The illustrative embodiment provides a computationally-efficient technique for reducing the number of possible positions that have to be analyzed. In particular, the illustrative embodiment eliminates possible positions for the mobile unit from consideration by considering which signals the mobile unit can—and cannot—receive and the knowledge of where those signals can and cannot be received.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is a continuation-in-part of U.S. patentapplication Ser. No. 09/158,296, filed Sep. 22, 1998, U.S. patentapplication Ser. No. 09/532,418, filed Mar. 22, 2000, and U.S. patentapplication Ser. No. 10/128,128, filed Apr. 22, 2002, all of which areincorporated by reference.

BACKGROUND OF THE INVENTION

[0002] The present invention relates generally to telecommunications,and more specifically to wireless communication systems.

[0003] In connection with mobile communication systems, it is becomingincreasingly important to determine the location of the communicatingMobile Unit. Various systems for locating are already well known. Onesolution that is readily available in most modern cellular systems is touse the ID of the cell from which the mobile unit is communicating.Typically, this information is accurate to a resolution of severalmiles. A second solution is to compute the location of the mobile unitbased on the cellular network signaling parameters (angle of arrival ortime difference of arrival). This information is typically accurate tohundreds of meters. Yet another solution is to equip the mobile unitwith a GPS receiver which then attempts to track the location of themobile unit as accurately as possible. Typically, GPS receivers cancompute locations to within several tens of meters of accuracy. Whencombined with differential corrections, the GPS accuracy can beimproved.

[0004] As far as reliability is concerned, the cell ID information isthe most reliable, and is guaranteed to be available as long as thecellular network is functioning normally. The network signal basedlocation computations are less reliable, since they are dependent onseveral conditions being true at the time of the call. For example, mostschemes require the mobile unit to have line-of-sight visibility tomultiple cellular base stations. This is not always possible. GPS basedlocation computation is also not always reliable since the mobile unitmay be in an environment where there is no penetration of the GPSsatellite signals.

SUMMARY OF THE INVENTION

[0005] The present invention provides a method for determining thelocation of a mobile unit without some of the costs and disadvantagesassociated with techniques in the prior art. In the prior art, a verylarge number of possible positions might have to be analyzed todetermine if the mobile unit is there and this can be computationallycumbersome. Therefore, any computationally-efficient technique thatreduces the number of possible positions that have to be analyzed isadvantageous. The illustrative embodiment provides acomputationally-efficient technique for reducing the number of possiblepositions that have to be analyzed. In particular, the illustrativeembodiment uses three logical tests to reduce the number of possiblelocations whose fingerprints must be considered. The three tests are:

[0006] Test #1—Eliminate all locations outside the serving area of thecell that is reported as the serving cell.

[0007] Test #2—Eliminate all locations outside the neighbor area of eachof the cells that is reported as a neighbor cell.

[0008] Test #3—Eliminate all locations at which an unreported neighboris known to be significantly stronger than a reported neighbor.

[0009] Some embodiments of the present invention also improve locationaccuracy by eliminating potentially ambiguous positions fromconsideration.

[0010] The illustrative embodiment comprises: a method for estimatingthe location of a mobile unit from a plurality of possible positions,the method comprising: receiving an indication from the mobile unit thatthe mobile unit does not receive a first signal from a firsttransmitter; eliminating some of the plurality of possible positionsfrom consideration based on the fact that the mobile unit did notreceive the first signal from the first transmitter; and choosing as anestimate of the position of the mobile unit one of the plurality ofpossible positions not eliminated from consideration based on the factthat the mobile unit did not receive the first signal from the firsttransmitter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011]FIG. 1 shows a representative wireless communication system;

[0012]FIG. 2 is a high level diagram of the Mobile Unit;

[0013]FIG. 3 is a flow diagram of the position determining processemployed by this invention;

[0014]FIG. 4 is a block diagram showing the technique for implementingcorrections for co-channel and adjacent-channel interference;

[0015]FIG. 5 is a block diagram describing the technique for calibratingthe predicted fingerprint database using field measurements;

[0016]FIG. 6 is a block diagram showing the operation of the recursiveprocessing Markov algorithm;

[0017]FIG. 7 describes the Markov transition probabilities;

[0018]FIG. 8 is a block diagram showing the operation of the multiplehypothesis testing algorithm;

[0019]FIG. 9 is an illustration of the organization of the fingerprintdata; and

[0020]FIG. 10 is an illustration of the organization of the fingerprintdatabase.

[0021]FIG. 11 depicts a map of a geographic region that is serviced by atypical wireless telecommunications system.

[0022]FIG. 12 depicts a flowchart of a technique in accordance with theillustrative embodiment.

[0023]FIG. 13 depicts a map of the serving area and the neighbor areaassociated with a cell site.

[0024]FIG. 14 depicts a map of the serving areas and the neighbor areasassociated with Cell Site A, Cell Site B, and Cell Site C.

[0025]FIG. 15 depicts a map of the locations that can be eliminatedbecause they are outside the serving cell's serving area.

[0026]FIG. 16 depicts a map of the locations that can be eliminatedbecause they are outside the reported neighbor cell's neighbor area.

[0027]FIG. 17 depicts a map of the locations that can be eliminatedbecause an unreported signal is known to be stronger than one of thereported signals.

[0028]FIG. 18 depicts a map of all of the locations that can beeliminated in accordance with FIGS. 15, 16, and 17.

[0029]FIG. 19 depicts a graph that illustrates the logic that underlieshow the illustrative embodiment eliminates some locations fromconsideration given the relative signal strength of two signals.

DESCRIPTION OF SPECIFIC EMBODIMENTS

[0030] The present invention provides a new method for determining thelocation estimate of a Mobile Unit (mobile unit) in a wirelesscommunication network.

[0031]FIG. 1 is a high level block diagram of a wireless communicationnetwork. A Mobile Unit 10 has a connection with a wireless network 15,which in turn is connected to location server 30. The location servermay or may not be mobile. The location of the mobile unit is of interestto the location server for several reasons such as provisioning ofprompt and efficient personalized services, dispatching emergencyassistance personnel, tracking the movements of the mobile unit, etc.

[0032] There are several different prior art methods for determining thelocation of mobile unit 10, as is known to one skilled in the art. Forexample, the mobile unit could be equipped with a GPS receiver.Alternatively, the wireless network could be equipped to determine thelocation of mobile unit 10. For example, the network could monitor thetime of arrival of signals from the mobile unit at various nodes andfrom that information determine its location. Again, such techniques arewell known to one skilled in the art.

[0033] All of the prior art techniques have significant disadvantages.For example, it is well known that GPS receivers do not work very wellin urban canyons and indoor locations where signal strength is very low.The network based schemes such as TDOA and AOA (both well known priorart) are disadvantaged in that they need significant infrastructuremodifications.

[0034] The present invention provides a new method for determining thelocation of mobile unit 10 which (a) works in areas where GPS coverageis not typically available, and (b) does not require any infrastructuremodifications. Thus, the present invention complements existing locationdetermining technologies and, when used in conjunction with them,augments their performance.

[0035] The invention is based on the principle that any location has aunique Radio Frequency (RF) spectral fingerprint. Spectral fingerprintin this context is defined as a predetermined combination of observableRF spectral parameters. For instance, observed signal strength of apredetermined set of signals in the RF spectrum constitutes afingerprint. Today, worldwide, practically the entire RF spectrum, up to2 GHz and above, is being utilized by several different applications.The signal characteristics vary greatly across this spectrum. However,for any given location, it is possible to pre-select a portion of thespectrum and a combination of signal parameters in the pre-selected bandthat will be unique to that location.

[0036] In accordance with the invention mobile unit 10 is equipped withcircuitry and software that is capable of capturing information frompredetermined portions of the RF spectrum. In one embodiment thepredetermined portions of the RF spectrum all fall within or in closeproximity to the same band as that utilized by the wirelesscommunication network. In such an instance the same hardware circuitrycan be used for performing both functions. In another embodiment thepredetermined portions of the RF spectrum are different from thewireless communication band and in such an instance additional circuitryis required. For example, the mobile unit may use signal characteristicsfrom the television UHF band, in which case it will require a televisiontuner capable of capturing the appropriate television channels. Inanother example the mobile unit is equipped with a tuner designed tocapture AM or FM radio broadcasts. In this case the mobile unit isequipped with a radio capable of tuning to the appropriate radiobroadcasting bands.

[0037]FIG. 2 shows the mobile unit containing a component 101 for tuningto a predetermined portion of the RF spectrum. Also included is acommunication component 105 for communicating information with thelocation server over an existing wireless infrastructure. Component 101obtains information from the RF spectrum via an Antenna 102. In oneembodiment of the system, the communication link between the mobile unitand location server is through the base stations and base stationcontrollers of the cellular network. In another embodiment of thesystem, data is communicated in both directions between the mobile unitand location server by using the Short Messaging System (SMS) of thenetwork. Using SMS messages for implementation of this invention has theadvantage of avoiding potential interference with voice channel networkoperations.

[0038] In many instances, location server 30 is interested in onlydetermining if mobile unit 10 is at a particular location or not. Theresolution of knowing the mobile unit's location is not high (e.g.,several meters), but much coarser, such as of the order of several tensof meters. For example, location server 30 may be interested in knowingif mobile unit 10 is inside a particular building, or a campus or ablock. In such cases it is not necessary to provide very high-resolutioninformation to location server 30.

[0039] There are other instances where location server 30 is desirous ofknowing the accurate location of mobile unit 10, however, is incapableof doing so. This could be because other location determiningcapabilities in the system, such as GPS, are not functional at theinstant when the location information is desired. This is typical whenthe mobile unit is in an area where GPS signals are not available, suchas inside a building. The location determining method described in thisinvention is capable of operating in areas where GPS and other locationtechnologies are not.

[0040] When a location estimate of the mobile unit is desired (either bythe mobile unit itself or by the location server), it activatescomponent 101 (FIG. 2), which captures predetermined information from apredetermined portion of the RF spectrum. Instructions regarding whatinformation to capture and the portion of the RF spectrum from which tocapture may be either pre-programmed in the mobile unit, or generated inreal time. In the latter case, it may be generated in the mobile unit,or downloaded into the mobile unit from the location server over thewireless network. The mobile unit may capture multiple pieces ofinformation or from multiple portions of the spectrum.

[0041] The spectral fingerprint may be generated using many differentparameters, either individually or in combination. In one embodiment,signal strength is used. In another embodiment, phase information isused. In another embodiment, the identity of the received signals (e.g.,frequency) is used. In yet another embodiment, the identity of thesignal source (e.g., channel number or station code) is used. In yetanother embodiment, the geographic locations of the transmitters fromwhich the signals originate are used.

[0042] A mobile cellular channel is in general frequency selective,i.e., its properties vary over the bandwidth used. The variation dependson the environment, because of multipath signals arriving at differentdelays. For a GSM signal with a bandwidth about 250 kHz, the dispersioncan be felt in urban and hilly environments. It causesinter-symbol-interference, which results in digital errors unlesscorrected. The time dispersion is often expressed as the delay spread orthe standard deviation of the impulse response. The remedy in a receiveris to have an equalizer, typically a transversal filter with some tapsthat can be adjusted. This filter will remove theinter-symbol-interference. The filter's tap values depend on theenvironment and impulse response, and thus in principle add additionalinformation to the RF fingerprint. For future systems which use W-CDMA(wideband code division multiple access), the bandwidth is more than 10times higher, of the order 4-5 MHz. The resolution power is thus muchhigher, and the various filter taps contain much more information. Inthis embodiment of the invention, the spectral characteristics withinthe used bandwidth (and not just individual center frequencies), asmeasured by the equalizer filter, will be included in the RFfingerprint.

[0043] The mobile unit is equipped with the appropriate circuitry andsoftware to capture the required signals and their parameters. In oneembodiment the mobile unit has an antenna that is designed to have abandwidth spanning a large portion of the VHF and UHF spectrum, e.g.,from 70 MHz to 1 GHz. In another embodiment, the mobile unit has anantenna that is designed to capture only a narrowband of the spectrum.Such an antenna may be cheaper to implement and unobtrusive. In oneembodiment the mobile unit is equipped with appropriate circuitry todetermine the strength of the received signal. In one instance thelocation of the transmitter is broadcast in the signal and is extractedin the mobile unit.

[0044] In one embodiment, the mobile unit is instructed by the locationserver to scan selected portions of the spectrum and capture selectedparameters from the received signals. The location server determineswhich portions of the spectrum to scan and what parameters to capturebased on other information it has received or generated regarding themobile unit. For example, in one instance the location server knows theapproximate location of the mobile unit by receiving identity of the(wireless communication network) cell that the mobile unit is in at thattime. By looking up a database the location server can determine thegeographic location of the cell. The location server then determineswhich signals in the vicinity of the cell are most suitable forgenerating a fingerprint. For example, certain television signals mayhave better coverage of the cell than other signals. The cellularnetwork then transmits this information (e.g., television channelnumbers) to the mobile unit via the wireless link requesting it to scanonly those selected signals.

[0045] In another embodiment, the mobile unit determines which portionof the spectrum to scan, and what parameters to use for generating thefingerprint.

[0046] After the mobile unit captures the appropriate signals andextracts the parameters, it has the basic information for generating thefingerprint. Some preprocessing may be required to refine the raw data.For example, signal strengths may have to be lower and upper limited toeliminate very weak and very strong signals.

[0047] Once the fingerprint is generated, its association with a certainlocation has to be determined. According to this invention this is doneby utilizing a fingerprint database that contains a number offingerprints along with their corresponding location identities. In oneembodiment the database is stored in the mobile unit. The generatedfingerprint is compared with the fingerprints in the database and thefingerprint in the database that is closest to the generated fingerprintis selected as the match. The corresponding location in the database isthen chosen as the location of the mobile unit. In one embodiment, thesearch algorithm takes more than one fingerprint from the database thatare closest to the generated fingerprint and interpolates the mostplausible location for the mobile unit from the locations of the chosenfingerprints.

[0048] In another embodiment, the fingerprint database is stored at thelocation server and the generated fingerprint (in the mobile unit) istransmitted to the location server over the wireless link. The searchfor the closest fingerprint is then done in the location server fromwhich it determines the location of the mobile unit.

[0049]FIG. 3 depicts the flow of events in this case. A request forposition of the mobile unit is generated, as shown in box 301. Therequest may be generated by the user carrying the mobile unit, orremotely by the location server. On receipt of the request the mobileunit captures the fingerprint of its current location (box 303). Thecaptured fingerprint is processed appropriately. Processing may includefiltering the fingerprint data and reformatting it to reduce its storagespace. Subsequently the fingerprint is transmitted over the wirelesslink to the location server as shown in box 305. The location server hasa database into which it executes a search for the closest matchingfingerprint, as shown in box 307. Box 309 shows the process culminatingin the retrieval of the best matching fingerprint along with itscorresponding location. In one embodiment the search also returns aconfidence measure that depicts the closeness of the match.

[0050] According to one implementation, the fingerprint database isdesigned to compensate for any co-channel and adjacent-channelinterference measured by the mobile unit (mobile unit). This channelinterference can result from either control channels or trafficchannels. To improve the accuracy, as well as viability, of anyalgorithm for mobile unit location based on measurements of base stationsignal strengths, corrections for channel interference will be required.These interference corrections are calculated and updated off-line usingRF prediction models (the same models employed to generate the originalfingerprint database) and the wireless carrier's frequency/channelallocation plan (FCAP). In one embodiment of the invention, theinterference corrections are implemented in the fingerprint database,which is stored in the location server.

[0051]FIG. 4 describes the data flow and processing requirements forthis technique. The data required from the wireless carrier's FCAP is asfollows: cell global identifiers, frequency identifiers for the assignedbase station control channels, and frequency identifiers for theassigned traffic channels. Co-channel interference sources areidentified using the FCAP to identify base stations that are assignedthe same frequency channel; the corrected signal strengths in eachchannel are then calculated. Adjacent-channel interference sources areidentified using the FCAP to identify base station pairs that areassigned adjacent frequency channels; the corrected signal strengths arethen calculated by also using the mobile unit's filter discriminationratio. The resulting interference-corrected fingerprints are stored inthe fingerprint database.

[0052] According to one implementation, the fingerprint database isgenerated by computer calculations with RF prediction models usingcertain parameters which depend on the type of buildings, terrainfeatures, time of the day, environmental and seasonal conditions, etc.The calculations are based upon theoretical models which have generalapplicability, but cannot consider all of the details in the real world.The accuracy of the fingerprint database can be enhanced by comparingthe model predictions with field measurements as shown in FIG. 5. Theadaptation algorithm analyzes the nature of the difference between thepredicted and measured fingerprints and determines the best way ofchanging the model parameters to reduce the difference. Since thedatabase consists of multiple frequencies, the difference is amulti-dimensional vector. The minimization of the difference can use acombination of advanced computational techniques based uponoptimization, statistics, computational intelligence, learning, etc.

[0053] The adaptation algorithm also determines what measurements tocollect to best calibrate the RF prediction model. Instead of collectingall measurements, only measurements that can provide the maximalinformation to support reduction of the prediction errors are collected.The specific choice of measurements to collect is based upon an analysisof the nature of the error and the desired accuracy of the predictionmodel. The algorithm determines not just the locations for measurementcollection, but also the time of collection, and the number ofmeasurement samples needed to achieve a certain degree of accuracy basedupon statistical considerations.

[0054] To improve the accuracy of the fingerprint database,learning/training techniques and methodologies can be adopted for thisproblem. For this embodiment of the invention, either Support VectorMachines for Regression or Regularized Regression can be used to learneach mapping of position versus fingerprint (measured or predictedsignal strengths). Position will be used as the input value, whilesignal strength will be used as the target value. The complete grid oflocations for the cellular service area will be used as training data.The final result is a learned relationship between position and signalstrength for each mapping so that, given an arbitrary position, thecorresponding signal strength is obtained for each particular frequencychannel.

[0055] The inputs of the training step are a set of positions matchedwith the corresponding signal strengths, and the fingerprintmeasurements whose information should be considered in the learningprocess are added to the training set. The machine is then re-trainedand a new grid is generated for the database, to replace the old one.The following list summarizes the major advantages of learning/trainingtechniques: (1) they offer a way to interpolate between grid points andreconstruct the grid, together with metrics to relate measurements andexpected fingerprints; (2) a local improvement of the neighboring arearesults from adding one measurement to the training set, and not onlythe particular location is affected, therefore, there is no need toobtain measurements for the complete grid; (3) measurements taken atdifferent times can help to produce specific databases for differenttimes of the day, different weather conditions, or different seasons;and (4) the system can learn which are the ambiguous areas with anintelligent agent, and collect measurements for them, so the databasecan be continually improved.

[0056] According to one implementation, the fingerprint database isdesigned to take into account any dynamic, but predetermined, variationsin the RF signal characteristics. For example, it is not uncommon thatsome AM radio broadcast stations lower their transmitter power at nightto minimize interference with other stations. In some countries this ismandated by law. If signal strength is one of the parameters used forgenerating the fingerprint, then it is essential that the dynamic changein transmitted power be taken into consideration before any decision ismade. According to this aspect of the invention, the fingerprintdatabase and the decision algorithms are designed to accommodate suchdynamic changes. Since the change pattern in signal characteristics ispredetermined, the database is constructed by capturing the fingerprintsat different times so as to cover all the different patterns in thetransmitted signals. The time at which a fingerprint was captured isalso stored along with its location identity.

[0057] There are many choices for the search algorithm that is requiredto determine the closest matching fingerprint, as can be appreciated byone skilled in the art of statistical pattern matching. Specifically,the choice of the algorithm is a function of what parameters are used togenerate the fingerprints. In one instance the search algorithm choosesthe fingerprint from the database that has the smallest mathematicaldistance between itself and the captured fingerprint. The mathematicaldistance is defined as a norm between the two data sets. For example, itcould be the average squared difference between the two fingerprints.There are many different ways to define “closeness” between twofingerprints; again, this is dependent on the signal parameters used togenerate the fingerprints. In one embodiment the search algorithm alsohas built in heuristics that make the best possible decision in casenone of the fingerprints in the database matches well with the generatedfingerprint.

[0058] Mobile unit (mobile unit) location determination that separatelyprocesses single time samples of the first fingerprints (i.e., measuredfingerprints) will herein be referred to as “static location,” which hasbeen shown to be an inherently inaccurate solution. Using a processingapproach based on the time series nature of the measured fingerprints,herein referred to as “dynamic location,” can yield significantimprovements in location accuracy with respect to static locationtechniques. In this embodiment of the invention, a dynamic locationtechnique, which is based on Markov theory and exploits the time historyof the measured fingerprints, is described in FIGS. 6 and 7. Theprocedure uses a predicted fingerprint model and a time series offingerprint measurements from the mobile unit to calculate theprobability distribution of the mobile unit's location. The probabilitydistribution is then used to compute an estimate of the current locationof the mobile unit using one of several statistical methods.

[0059] As shown in FIG. 6, the processing algorithm is recursive anduses a predefined array of possible locations. When the algorithm hasprocessed the time series of fingerprint measurements currentlyavailable, it will have calculated the probability that the mobile unitis at each point in the array of possible locations (the updatedprobability distribution). This probability distribution is the basicstate of the algorithm that incorporates all of the information that hasbeen collected on the mobile unit up to the current time. When a newfingerprint measurement becomes available, the algorithm uses itsknowledge of the possible motions of the mobile unit during the timeperiod between the last measurement and the new measurement to predict anew location probability distribution (the predicted distribution). Thisprediction algorithm is based on a Markov process model, which uses theprobability that the unit is at each location and the probability thatit will transition from one location to another to calculate theprobability that it will be at each location at the next measurementtime (see FIG. 7). Next, the algorithm uses the predicted signaturemodel and the new signature measurement to calculate a locationprobability distribution based on the new measurement alone. Thisprobability distribution is then combined with the predicteddistribution to produce a new updated distribution that now incorporatesthe information provided by the new measurement.

[0060] Before any measurements are available, the algorithm starts withan initial probability distribution that assumes that the mobile unit isequally likely to be at any location in the cell for the base stationhandling the call. The location probability distribution (either updatedor predicted) may be used to compute an estimate of the mobile unit'slocation whenever such an estimate is needed. The basic algorithmcalculates both the expected location and the most probable location,but other location statistics (as deemed appropriate) could also becalculated from the location probability distribution. The locationestimate may be calculated for either the time of the last fingerprintmeasurement (which yields a filtered estimate) or for some time beforethe last fingerprint measurement (which yields a smoothed estimate). Thesmoothed estimate is more accurate than the filtered estimate, becauseit uses more information (i.e., some number of subsequent measurements).However, the smoothed estimate requires both a larger state and morecomputation than the filtered estimate.

[0061] Matching the measured fingerprints to the fingerprint databasemay yield multiple location possibilities in the presence of measurementand modeling errors. A single hypothesis approach that selects the bestlocation estimate for the mobile unit using only the current measuredfingerprint is likely to produce erroneous results. In this embodiment,a multiple hypothesis testing technique uses measured fingerprints frommultiple times, the fingerprint database, and mobile unit motionconstraints to generate multiple hypotheses for mobile unit locationsover time and then selects the best hypothesis based upon statisticalmeasures.

[0062] In FIG. 8, the measured fingerprints are compared with thefingerprint database to generate possible mobile unit locationhypotheses. When previous location hypotheses are available, thesehypotheses are updated with the latest measurements to form newhypotheses. The number of hypotheses is reduced by considering onlythose locations that are consistent with the previous locationhypotheses. A previous location hypothesis may yield multiple newhypotheses because of possible new locations. However, some previouslocation hypotheses may not have consistent new locations based on thenew measurements. A hypothesis evaluation algorithm then assigns a scoreto each of the updated location hypotheses. This score measures how wellthe locations at different times in the hypothesis are consistent withreasonable mobile unit motion and measurements characteristics. Theevaluation of this score is based upon statistical techniques and usesmobile unit motion constraints, the fingerprint database, and themeasured fingerprint characteristics. The score also depends on thescore of the previous hypothesis. The location hypotheses are thenranked according to this score. A hypothesis management algorithmreduces the number of hypotheses by removing hypotheses with low scoresand combining similar hypotheses. The surviving hypotheses are thenupdated with new measurements.

[0063] If the search takes too long to complete, the resulting answerwill be stale and ordinarily of little value. Therefore, theillustrative embodiment seeks to shorten the time that the search takesto complete.

[0064] The length of time that the search takes to complete is relatedto its computational complexity. Furthermore, the computationalcomplexity of the search is related to the number of fingerprints thatmust be considered, and the number of fingerprints that must beconsidered is related to the number of possible locations where themobile unit could be. Therefore, the length of time that the searchtakes to complete is related to the number of possible locations of themobile unit that must be considered.

[0065] As the number of possible locations to search increases, thelength of time to complete the search also increases. Analogously, asthe number of possible locations to search decreases, the length of timeto complete the search also decreases. Therefore, the illustrativeembodiment seeks to reduce the length of time it takes to complete thesearch by reducing the number of possible locations that must besearched.

[0066] The number of possible locations of the mobile unit that must besearched can be reduced when an approximate estimate of the mobileunit's location is available. For example, an approximate estimate ofthe mobile unit's location can be determined from the identity of thecell site serving the mobile unit. This can, for example, reduce thenumber of possible locations by a factor of one thousand or more.

[0067] Similarly, the number of possible locations that must be searchedcan be reduced by knowing which signals the mobile unit has reported itis able to receive and distinguish. This concept is described in detailbelow and with regard to FIGS. 11 through 19.

[0068]FIG. 11 depicts a map of a geographic region that is serviced by awireless telecommunications system. In this wireless telecommunicationssystem, there are three omnidirectional cell sites, Cell Site A, CellSite B, and Cell Site C, that are responsible for providing service tothe mobile units (e.g., mobile unit 1101) within the purview of thesystem.

[0069] At any given moment, each mobile unit is at one of a number oflocations, and the flowchart depicted in FIG. 12 describes a method forestimating the location of the mobile unit so that the number offingerprints to be searched can be reduced.

[0070] Initially, the location server maintains a database that listsall of the possible locations for the mobile unit. Table 1 depicts aportion of such an illustrative database. The database correlates toeach location:

[0071] i. the fingerprint associated with that location,

[0072] ii. the identities of the cell sites that could serve thatlocation,

[0073] iii. the identities of the cell sites that could be received asneighbors at that location.

[0074] How this information is used will be described in detail below.For example, the fingerprint is the signal, so it doesn't need to besaved again. Furthermore, in the illustrative embodiment the absolutesignal strength is saved and not the relative signal strength becausethe calculation of relative signal strength must be done in real-timebecause it depends on which signals are reported (i.e., signal strengthof one signal relative to another reported signal) and there are toomany possible combinations of reported and unreported signals. It willbe clear to those skilled in the art how to compile and maintain such adatabase. TABLE 1 Database of Possible Locations of Mobile Unit PossiblePossible Serving Neighbor Cell Cell Location Latitude LongitudeFingerprint Site(s) Site(s)    1 47° 10′ 127° 23′ F₁ A, B B, A 23.12″ N.41.73″ W. . . . . . . . . . . . . . . . . . . 60,000 47° 13′ 127° 26′F_(60,000) C A, C 46.33″ N. 44.53″ W.

[0075] In accordance with the illustrative example, and as shown inTable 1, the mobile unit is at one of sixty thousand (60,000) possiblelocations. It will be clear to those skilled in the art, however, afterreading this specification, that other systems might comprise adifferent number of locations. Although the location server couldcompare the RF fingerprints received from the mobile unit to all 60,000possible locations, the method depicted in FIG. 12 eliminates some ofthe 60,000 possible locations based on which signals the mobile unit isable to receive and the relative strength of those signals in everylocation.

[0076]FIG. 12 depicts a flowchart of the tasks associated with reducingthe number of possible locations that must be searched in accordancewith the illustrative embodiment. Tasks 1201 though 1203 are normallyperformed by the cellular network as a part of its operation. Task 1207is the operation of the base location server described in thisspecification. Tasks 1204 through 1206 are elements of the presentinvention, and their purpose is to reduce the number of possiblepositions that must be considered by task 1207.

[0077] At task 1201, the cellular network determines one or more signalsthat the mobile unit might or might not be able to receive. Toaccomplish this, the cellular network comprises a database that listswhich signals might be receivable by the mobile unit.

[0078] In accordance with the illustrative embodiment, each receivablesignal is a base station control channel, which can be distinguishedbased on a Base Station Identity Code. It will be clear to those skilledin the art how to make and use embodiments of the present invention thatare keyed to a different signal.

[0079] At task 1202, the cellular network directs the mobile unit toattempt to receive the signals in Table 2 and to and report back asignal strength value for the strongest (up to) M signals that themobile unit is able to receive and distinguish, wherein M is a positiveinteger. For example, when M=3 and the mobile unit is able to receiveand distinguish 4 signals, the mobile only reports back the signalstrength for the 3 strongest. On the other hand, when M=3 and the mobileunit is able to receive and distinguish 2 signals, the mobile onlyreports back the signal strength for the 2 signals.

[0080] In accordance with the illustrative embodiment, consider thesituation where Cell A is the serving cell (i.e., the cell currentlyhandling the communications traffic to and from the mobile) and thecellular system directs the mobile to monitor signals on controlfrequencies of Cell B and Cell C and to report the strongest of thesetwo neighbors that it can distinguish. Note that the mobile canobviously distinguish the signal from Cell A; otherwise Cell A could notbe acting as the serving cell.

[0081] At task 1203, the cellular network receives the signal strengthreport from the mobile unit in response to the direction in task 1202.With this information and the data in Table 1, the location server usesthree logical tests to reduce the number of possible locations whosefingerprints must be considered. The three tests are:

[0082] Test #1—Eliminate all locations outside the serving area of thecell that is reported as the serving cell.

[0083] Test #2—Eliminate all locations outside the neighbor area of thecells that are reported as neighbor cells.

[0084] Test #3—Eliminate all locations at which an unreported neighboris known to be stronger than a reported neighbor.

[0085] In accordance with the illustrative embodiment, the locationserver receives a report on 2 signals, Signal A=−11 dBm, which isidentified as being associated with the serving cell, and Signal B=−14dBm.

[0086] As shown in FIG. 13, there is an area called the “serving area”around each cell site where that cell site could serve a mobile unit.Furthermore, there is a larger area called the “neighbor area” aroundeach cell site where that cell site could be recognized by a mobile unitas a neighbor. In the illustrative embodiment, both the serving area andthe neighbor areas are depicted as circles with the cell site at theircenter. It will be clear to those skilled in the art how to make and useembodiments of the present invention in which the serving areas andneighbor areas have different shapes and sizes.

[0087]FIG. 14 depicts a map of wireless telecommunications system 1100,and the respective serving and neighbor areas for each of the three cellsites. From FIG. 14, it should be noted that some locations can beserviced by all three cell sites, some by only two cell sites, and someby only one cell site. Furthermore, some locations are neighbors fornone, one, or two cell sites.

[0088] At task 1204, in accordance with Test #1, the location servereliminates from consideration all locations outside of the servingcell's serving area.

[0089] Because Cell A is the serving cell, the location servereliminates from consideration all locations outside of Cell Site A'sserving area. This is depicted graphically in FIG. 15, and can becomputed from the list of possible serving sites in Table 1.

[0090] At task 1205, in accordance with Test #2, the location servereliminates from consideration all locations outside of the reportedneighbor cell's neighbor area.

[0091] In the illustrative embodiment, Signal B, which is associatedwith Cell Site B, was reported as a neighbor signal at task 1203.Therefore, as graphically depicted in FIG. 16, all locations outside ofthe neighbor area for Cell Site B are eliminated from consideration.This can be computed from the list of possible neighbor sites in Table1.

[0092] At task 1206, in accordance with Test #3, the location servereliminates from consideration all locations that would have beenreported in task 1203 had the mobile unit been, in fact, at thatlocation. For example, in the illustrative embodiment the mobile unitwas directed to report the stronger or two signals from neighbors B andC and the mobile unit reported Signal B. Therefore, the location servercan logically eliminate from consideration all locations where Signal Cis known to be stronger than Signal B. This can be computed from thefourth column of Table 1.

[0093] Because neighbor Signal B was reported in task 1203 and neighborSignal C was not, FIG. 17 depicts a map of the locations that can beeliminated because Signal C is stronger than Signal A at thoselocations.

[0094] In summary, FIG. 18 depicts a map of the locations that can beeliminated through Test #1, #2, or #3, and, therefore, it also depictsthe locations whose fingerprints must be searched.

[0095] Because there is a range of uncertainty in the measurement of allsignals, the boundary of which locations can—and which cannot—mustconsider that range of uncertainty. FIG. 19 depicts a graph thatillustrates the logic that underlies which locations can be eliminatedbased on the range of uncertainty in the measurement of the signals.Taking this effect into account, the dividing line in FIG. 17 istechnically closer to cell site C than it is to cell site B, rather thanequidistant between the two.

[0096] At task 1207, the location server chooses as an estimate of thelocation of the mobile unit one of the possible locations not eliminatedfrom consideration in tasks 1204, 1205, or 1206, and as depicted in FIG.18. Furthermore, the search complexity is reduced by noting the time atwhich the location information is requested.

[0097]FIG. 9 illustrates a structure 400 of the fingerprint in oneembodiment. As mentioned previously there are several possible methodsfor defining the fingerprint; FIG. 9 is but an example. The time atwhich the fingerprint is captured is stored in the fingerprintstructure, as shown by box 401. In one embodiment the UTC format is usedto store time. There are several fields in the structure, some of whichare optionally filled by the mobile unit. Some other fields areoptionally filled by the location server. It is not necessary that allfields be filled, since the necessary fields can be predetermined basedon system parameters.

[0098] The fingerprint comprises characteristics of received signals atmultiple frequencies. Each column in FIG. 9 is information pertaining toa particular frequency or carrier. A Station ID field 403 indicates theunique identifying code of a broadcasting station from which the signalemanated. This field is optional. In one embodiment this field is filledby the mobile unit using information received in the signal. In anotherembodiment this field is filled by the location server to indicate tothe mobile unit as to which signals to capture for the fingerprint. AFrequency field 405 is the unique frequency value at which a signal iscaptured. Either the Station ID field or the Frequency field ismandatory since without both it is not possible to identify the signal.A Tuning Parameter field 407 is used when the mobile unit requiresadditional information to tune to a particular carrier. In oneembodiment this field is supplied by the location server withinformation containing the modulation characteristics of the signal.This field is optional. In one embodiment a Transmitter Location field409 is used to characterize the received signals. In another embodimentthis field is filled by the location server. The mobile unit mayoptionally use this information to determine if it wants to capture thesignal emanating from a particular transmitter. Finally, Signal Strengthfields 411, 413, are filled by the mobile unit based on the signalstrengths of the received carriers. In one embodiment the signalstrength is sampled multiple times for each frequency in order to smoothout any variations. At least one of the Signal Strength fields isrequired to be filled by the mobile unit.

[0099]FIG. 10 shows the high level structure of the fingerprint database501 in one embodiment. As one skilled in the art can appreciate, thereare many methods for building, managing and searching databases. Thepurpose of FIG. 10 is merely to illustrate the structure of the databasein one embodiment. Each row in database 501 corresponds to onefingerprint. The Lat and Long fields indicate the latitude and longitudeof the location to which the fingerprint corresponds. In one instancethe fingerprint corresponds not to one exact spot on the surface of theearth, but instead to a small area. The Lat and Long fields in thisembodiment indicate a position inside the area, preferably the centerpoint. The Time column indicates the time at which the fingerprint wascaptured. In one embodiment the UTC time format is used to indicate thistime. The Fingerprint column contains the actual fingerprint data. Inone embodiment the structure depicted in FIG. 9 is used to store thefingerprint data. Finally, the Description column contains a shortdescription of the location corresponding to the fingerprint. Forexample, it may indicate a street address, or an intersection. Thisfield is optional.

[0100] In one embodiment, the database is built by taking off-linesnapshots of fingerprints at various locations. The fingerprintinformation along with the coordinates of the location are entered intothe database. The more the locations, the richer the database. Theresolution of location determination is also controlled by how far apartthe fingerprint samples are taken. The closer they are, the higher theresolution. Of course, a person skilled in the art can appreciate thatthe resolution of the database is limited by the sensitivity of thefingerprint measuring device. In one embodiment the fingerprints aretaken using very sensitive signal measuring devices that enablelocations that are very close to each other to have distinctfingerprints.

[0101] In another embodiment, the database is built by takingfingerprint measurements at predetermined locations and usingintelligent algorithms that interpolate the fingerprints at alllocations in between the sampled locations. This method has theadvantage of not requiring a great many physical measurements to bemade, however, it does suffer from some loss in accuracy. This isbecause, however clever, the interpolating algorithms will not be asaccurate as making the actual measurements.

[0102] In yet another embodiment, the database is generated on-lineusing smart algorithms that can predict the fingerprints in a localarea. This scheme is effective in instances where an approximate idea ofthe mobile unit location is already available. For example, this couldbe the cell in which the mobile unit is located.

[0103] Location estimation procedures using a predicted fingerprintmodel and fingerprint measurements from the mobile unit are subject toseveral sources of error, including errors in measuring the fingerprintsand mismatches between the fingerprint model and the real world. If thepredicted fingerprints in two locations are similar, either of theseerror sources may cause the measured fingerprint to more closely matchthe fingerprint model for the incorrect location than for the correctlocation. This situation leads to the possibility of fingerprintambiguities.

[0104] For this embodiment of the invention, when a fingerprint model isproduced for a particular region, it can be subjected to statisticalanalysis to assess both the location accuracy that may be expected inthat region and to identify specific areas that can have potentialambiguity problems. For each point in the fingerprint model, theprobability distribution of ambiguous locations may be calculated, as afunction of the level of errors expected, by computing the likelihoodthat each pair of location points will be confused at that error level.The characteristics of this distribution provide information about theaccuracy and confidence of location estimates. If the distribution at aparticular point is tight (low statistical variance), location estimatesof mobile unit's at that location should be quite accurate. If thedistribution at a particular point is very broad (high statisticalvariance), location estimates of mobile unit's at that location arelikely to be inaccurate. Conversely, if the calculated location estimateis at a point known to have high ambiguity (high statistical variance),the confidence attributed to that estimate should be low.

[0105] The following references are incorporated by reference in theirentirety for all purposes:

[0106] 1. T. Suzuki, et al., The Moving-body Position Detection Method;Patent Application (Showa 63-195800), August 1988.

[0107] 2. M. Hellebrandt, et al., Estimating Position and Velocity ofMobiles in a Cellular Radio Network, IEEE Trans. On VehicularTechnology, Vol. 46, No. 1, pp. 65-71, February 1997.

[0108] 3. I. Gaspard, et al., Position Assignment in Digital CellularMobile Radio Networks (e.g. GSM) Derived from Measurements at theProtocol Interface, IEEE Journal, pp. 592-596, March 1997.

[0109] 4. J. Jimenez, et al., Mobile Location Using CoverageInformation: Theoretical Analysis and Results, COST 259 TD (98), April1999.

CONCLUSION

[0110] In conclusion, it can be seen that this invention has one or moreof the following significant improvements over prior art for mobile unitlocation techniques: (1) it can be implemented without requiring anymodifications to the existing wireless network infrastructure, (2) onlyminor software changes in the mobile unit (typically a cellular phone)are required, and (3) it can be utilized in areas where GPS coverage iseither not available or not reliable. This follows because thefingerprints are generated by using portions of the RF spectrum thattypically have superior coverage and in-building penetration than GPSsignals.

[0111] While the above is a complete description of specific embodimentsof the invention, various modifications, alternative constructions, andequivalents may be used. Therefore, the above description should not betaken as limiting the scope of the invention as defined by the claims.

What is claimed is:
 1. A method for estimating the location of a mobileunit from a plurality of possible positions, said method comprising:receiving an indication from said mobile unit that said mobile unit doesnot receive a first signal from a first transmitter; eliminating some ofsaid plurality of possible positions from consideration based on thefact that said mobile unit did not receive said first signal from saidfirst transmitter; and choosing as an estimate of said position of saidmobile unit one of said plurality of possible positions not eliminatedfrom consideration based on the fact that said mobile unit did notreceive said first signal from said first transmitter.
 2. The method ofclaim 1 wherein said indication from said mobile unit that said mobileunit does not receive a first signal from a first transmitter isinferred because said mobile unit failed to indicate that said mobileunit did receive said first signal.
 3. The method of claim 1 whereinsaid indication from said mobile unit that said mobile unit does notreceive a first signal from a first transmitter is explicit.
 4. Themethod of claim 1 further comprising: receiving an indication from saidmobile unit that said mobile unit does not receive a second signal froma second transmitter; eliminating some of said plurality of possiblepositions from consideration based on the fact that said mobile unit didnot receive said second signal from said second transmitter; andchoosing as an estimate of said position of said mobile unit one of saidplurality of possible positions not eliminated from consideration basedon the fact that said mobile unit did not receive said first signal fromsaid first transmitter or said second signal from said secondtransmitter.
 5. The method of claim 1 further comprising: receiving anindication from said mobile unit that said mobile unit did receive asecond signal from a second transmitter; eliminating some of saidplurality of possible positions from consideration based on the factthat said mobile unit did receive said second signal from said secondtransmitter; and choosing as an estimate of said position of said mobileunit one of said plurality of possible positions not eliminated fromconsideration based on the fact that said mobile unit did not receivesaid first signal from said first transmitter but did receive saidsecond signal from said second transmitter.
 6. A method for estimatingthe location of a mobile unit, said method comprising: receiving thecell ID of the cell site servicing said mobile unit; generating aplurality of possible positions in which said mobile unit might be basedon said cell ID; receiving an indication from said mobile unit that saidmobile unit did not receive a first signal from a first transmitter;eliminating some of said plurality of possible positions fromconsideration based on the fact that said mobile unit did not receivesaid first signal from said first transmitter; and choosing as anestimate of said position of said mobile unit one of said plurality ofpossible positions not eliminated from consideration based on the factthat said mobile unit did not receive said first signal from said firsttransmitter.
 7. The method of claim 6 wherein said indication from saidmobile unit that said mobile unit does not receive a first signal from afirst transmitter is inferred because said mobile unit failed toindicate that said mobile unit did receive said first signal.
 8. Themethod of claim 6 wherein said indication from said mobile unit thatsaid mobile unit does not receive a first signal from a firsttransmitter is explicit.
 9. The method of claim 4 further comprising:receiving an indication from said mobile unit that said mobile unit doesnot receive a second signal from a second transmitter; eliminating someof said plurality of possible positions from consideration based on thefact that said mobile unit did not receive said second signal from saidsecond transmitter; and choosing as an estimate of said position of saidmobile unit one of said plurality of possible positions not eliminatedfrom consideration based on the fact that said mobile unit did notreceive said first signal from said first transmitter or said secondsignal from said second transmitter.
 10. The method of claim 4 furthercomprising: receiving an indication from said mobile unit that saidmobile unit did receive a second signal from a second transmitter;eliminating some of said plurality of possible positions fromconsideration based on the fact that said mobile unit did receive saidsecond signal from said second transmitter; and choosing as an estimateof said position of said mobile unit one of said plurality of possiblepositions not eliminated from consideration based on the fact that saidmobile unit did not receive said first signal from said firsttransmitter but did receive said second signal from said secondtransmitter.
 11. A method comprising: receiving an indication from amobile unit of a plurality of signals that said mobile unit can receiveand a plurality of signals that said mobile unit cannot receive;generating a plurality of possible positions based on said plurality ofsignals that said mobile unit can receive and on said plurality ofsignals that said mobile unit cannot receive; and choosing as anestimate of said position of said mobile unit one of said plurality ofpossible positions by comparing the fingerprint associated with each ofsaid plurality of possible positions against a fingerprint received fromsaid mobile unit.
 12. The method of claim 11 wherein said indicationfrom said mobile unit that said mobile unit does not receive a firstsignal from a first transmitter is inferred because said mobile unitfailed to indicate that said mobile unit did receive said first signal.13. The method of claim 11 wherein said indication from said mobile unitthat said mobile unit does not receive a first signal from a firsttransmitter is explicit.
 14. A method for estimating a location of amobile unit, said method comprising: directing said mobile unit toreport a signal strength value for the strongest of a first plurality ofsignals; receiving a signal strength value from said mobile unit for asecond plurality of signals, wherein said second plurality of signals isa non-empty subset of said first plurality of signals; and eliminatingfrom consideration as said location of said mobile unit any location atwhich any of said second plurality of signals is known to be less strongthan any of said first plurality of signals not reported.
 15. The methodof claim 14 wherein a range of uncertainty is considered whendetermining when any of said second plurality of signals is comparedwith any of said first plurality of signals.